Pseudotime Analysis
In [ ]:
!date
Mon Apr 12 18:25:46 UTC 2021
Download Data¶
In [1]:
import requests
from tqdm import tnrange, tqdm_notebook
def download_file(doi,ext):
url = 'https://api.datacite.org/dois/'+doi+'/media'
r = requests.get(url).json()
netcdf_url = r['data'][0]['attributes']['url']
r = requests.get(netcdf_url,stream=True)
#Set file name
fname = doi.split('/')[-1]+ext
#Download file with progress bar
if r.status_code == 403:
print("File Unavailable")
if 'content-length' not in r.headers:
print("Did not get file")
else:
with open(fname, 'wb') as f:
total_length = int(r.headers.get('content-length'))
pbar = tnrange(int(total_length/1024), unit="B")
for chunk in r.iter_content(chunk_size=1024):
if chunk:
pbar.update()
f.write(chunk)
return fname
In [2]:
#Kallisto bus clustered starvation data, h5ad
download_file('10.22002/D1.1796','.gz')
#Starvation h5ad data, all nonzero genes included, filtered for 'real cells' from de-multiplexing
download_file('10.22002/D1.1797','.gz')
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`
Out[2]:
'D1.1797.gz'
In [3]:
#Previously saved neuron subpopulations
download_file('10.22002/D1.1804','.gz')
#Previously saved neuron pseudotime adata
download_file('10.22002/D1.1806','.gz')
#Previously saved nematocyte pseudotime adata
download_file('10.22002/D1.1805','.gz')
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`
Out[3]:
'D1.1805.gz'
In [4]:
#Human ortholog annotations
download_file('10.22002/D1.1819','.gz')
#Panther annotations
download_file('10.22002/D1.1820','.gz')
#GO Terms
download_file('10.22002/D1.1822','.gz')
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`
Out[4]:
'D1.1822.gz'
In [5]:
#Previously saved ranked pseudotime genes
#Nematocyte trajectory (ranked genes)
#!wget --quiet https://caltech.box.com/shared/static/u19vzppejvt6ky3ysqptt2ia2dqozpkf
download_file('10.22002/D1.1808','.gz')
#Neuron trajectory (ranked genes)
#!wget --quiet https://caltech.box.com/shared/static/ippfug3xt20etmyx08egzpipgmyfc17m
download_file('10.22002/D1.1807','.gz')
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`
Out[5]:
'D1.1807.gz'
In [6]:
!gunzip *.gz
In [7]:
#Install packages
!pip install --quiet anndata
!pip install --quiet scanpy==1.7.0rc1
!pip install --quiet louvain
#!pip install --quiet cellrank==1.3.0
|████████████████████████████████| 133kB 8.7MB/s
|████████████████████████████████| 10.2MB 6.6MB/s
|████████████████████████████████| 71kB 8.8MB/s
|████████████████████████████████| 71kB 9.2MB/s
Building wheel for umap-learn (setup.py) ... done
Building wheel for sinfo (setup.py) ... done
|████████████████████████████████| 2.2MB 8.9MB/s
|████████████████████████████████| 3.2MB 44.3MB/s
In [8]:
!pip3 install --quiet rpy2
Import Packages¶
In [9]:
#Import Packages
import random
import pandas as pd
import anndata
import scanpy as sc
import numpy as np
import scipy.sparse
#import cellrank as cr
import warnings
warnings.filterwarnings('ignore')
from sklearn.neighbors import (KNeighborsClassifier,NeighborhoodComponentsAnalysis)
from sklearn.pipeline import Pipeline
from sklearn.manifold import TSNE
from scipy import sparse, io
#import scrublet as scr
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rc('axes',edgecolor='black')
%matplotlib inline
sc.set_figure_params(dpi=125)
import seaborn as sns
sns.set(style="whitegrid")
%load_ext rpy2.ipython
In [10]:
#Read in annotations
from io import StringIO
hg_ortho_df = pd.read_csv(StringIO(''.join(l.replace('|', '\t') for l in open('D1.1819'))),
sep="\t",header=None,skiprows=[0,1,2,3])
hg_ortho_df[['XLOC','TCONS']] = hg_ortho_df[13].str.split(expand=True)
hg_ortho_df[['Gene','gi']] = hg_ortho_df[3].str.split(expand=True)
hg_ortho_df['Description']= hg_ortho_df[11]
panther_df = pd.read_csv('D1.1820',
sep="\t",header=None) #skiprows=[0,1,2,3]
goTerm_df = pd.read_csv('D1.1822',
sep=" ",header=None) #skiprows=[0,1,2,3]
In [11]:
#Read in saved data
bus_fs_combo = anndata.read('D1.1796')
print(bus_fs_combo)
bus_fs_raw = anndata.read('D1.1797')
bus_fs_raw.obs['cellRanger_louvain'] = pd.Categorical(bus_fs_combo.obs['cellRanger_louvain'])
bus_fs_raw.obs['fed'] = pd.Categorical(bus_fs_combo.obs['fed'])
bus_fs_raw.obs['orgID'] = pd.Categorical(bus_fs_combo.obs['orgID'])
bus_fs_raw.obs['annos'] = pd.Categorical(bus_fs_combo.obs['annos'])
bus_fs_raw.obs['annosSub'] = pd.Categorical(bus_fs_combo.obs['annosSub'])
bus_fs_raw.uns['annos_colors'] = bus_fs_combo.uns['annos_colors']
bus_fs_raw.uns['cellRanger_louvain_colors'] = bus_fs_combo.uns['annosSub_colors']
#bus_fs_raw.uns['annosSub_colors'] = bus_fs_combo.uns['annosSub_colors']
print(bus_fs_raw)
neuron_subpops = anndata.read('D1.1804')
print(neuron_subpops)
AnnData object with n_obs × n_vars = 13673 × 8696
obs: 'batch', 'n_counts', 'n_countslog', 'louvain', 'leiden', 'orgID', 'fed', 'starved', 'fed_neighbor_score', 'cellRanger_louvain', 'annos', 'new_cellRanger_louvain', 'annosSub'
var: 'n_counts', 'mean', 'std'
uns: 'annosSub_colors', 'annos_colors', 'cellRanger_louvain_colors', 'cellRanger_louvain_sizes', "dendrogram_['new_cellRanger_louvain']", 'dendrogram_new_cellRanger_louvain', 'fed_colors', 'fed_neighbor_score_colors', 'leiden', 'leiden_colors', 'louvain', 'louvain_colors', 'neighbors', 'new_cellRanger_louvain_colors', 'orgID_colors', 'paga', 'pca', 'rank_genes_groups', 'umap'
obsm: 'X_nca', 'X_pca', 'X_tsne', 'X_umap'
varm: 'PCs'
obsp: 'connectivities', 'distances'
AnnData object with n_obs × n_vars = 13673 × 46716
obs: 'batch', 'cellRanger_louvain', 'fed', 'orgID', 'annos', 'annosSub'
uns: 'annos_colors', 'cellRanger_louvain_colors'
AnnData object with n_obs × n_vars = 1387 × 2000
obs: 'batch', 'cellRanger_louvain', 'fed', 'n_counts', 'louvain_neur', 'test_louvain'
var: 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'cellRanger_louvain_colors', 'hvg', 'louvain', 'louvain_neur_colors', 'neighbors', 'pca', 'rank_genes_groups', 'test_louvain_colors', 'umap'
obsm: 'X_pca', 'X_tsne', 'X_umap'
varm: 'PCs'
obsp: 'connectivities', 'distances'
In [12]:
#Functions used
#Random forest regression to determine best genes to predict pseudotime
from sklearn.ensemble import RandomForestRegressor
from sklearn.inspection import permutation_importance
def runRegr_dptPseudotime(adata,adata_scaled,label='dpt_pseudotime'):
X = adata.X
if scipy.sparse.issparse(X):
X = X.toarray()
#80% training data
perc = 0.8
n = np.int(perc*X.shape[0])
np.random.seed(0)
indices = np.random.choice(X.shape[0], n, replace=False,)
X_train = X[indices,:]
y = adata_scaled.obs[label]
y_train = y[indices]
X_test = X[[i for i in range(0,X.shape[0]) if i not in indices],:]
y_test = y[[i for i in range(0,X.shape[0]) if i not in indices]]
regr = RandomForestRegressor(random_state=42,max_samples=perc)
regr.fit(X_train, y_train)
print(regr.score(X_test, y_test))
result = permutation_importance(regr, X_test, y_test, n_repeats=5, random_state=42)
return result
#Convert filtered results to dataframe
def resToDF(result,adata):
neur_gene_res = pd.DataFrame()
neur_gene_res['Genes'] = adata.var_names
neur_gene_res['Mean'] = result.importances_mean
neur_gene_res['Std'] = result.importances_std
neur_gene_res= neur_gene_res.sort_values('Mean', axis=0, ascending=False)
neur_gene_res= neur_gene_res[neur_gene_res.Mean > 0]
print(len(neur_gene_res.Genes))
return neur_gene_res
#Make dataframe, with 100 marker genes for each cluster + annotations
def annotateResDF(resultDF):
orthoGene = []
orthoDescr = []
pantherNum = []
pantherDescr = []
goTerms = []
genes = resultDF.Genes
for g in genes:
sub_df = hg_ortho_df[hg_ortho_df.XLOC.isin([g])]
panth_df = panther_df[panther_df[0].isin([g])]
go_df = goTerm_df[goTerm_df[0].isin([g])]
if len(sub_df) > 0:
#Save first result for gene/description
orthoGene += [list(sub_df.Gene)[0]]
orthoDescr += [list(sub_df.Description)[0]]
else:
orthoGene += ['NA']
orthoDescr += ['NA']
if len(panth_df) > 0:
pantherNum += [list(panth_df[1])]
pantherDescr += [list(panth_df[2])]
else:
pantherNum += ['NA']
pantherDescr += ['NA']
if len(go_df) > 0:
goTerms += [list(go_df[1])]
else:
goTerms += ['NA']
resultDF['orthoGene'] = orthoGene
resultDF['orthoDescr'] = orthoDescr
resultDF['pantherID'] = pantherNum
resultDF['pantherDescr'] = pantherDescr
resultDF['goTerms'] = goTerms
return resultDF
#list(neurons.uns['rank_genes_groups']['names']['1'])
#Order cells in cell-by-gene matrix by pseudotime
def orderTime(adata):
sortTime = np.argsort(adata.obs['dpt_pseudotime'])
newNames = adata.obs_names[list(sortTime)]
adataRet = adata[newNames,:]
return adataRet
In [13]:
def orderTimeClus(adata,clus = [0,12,11,23,17,10,5,21]):
names = []
for i in clus:
sub = adata[adata.obs['cellRanger_louvain'].isin([i])]
# sortTime = np.argsort(sub.obs['dpt_pseudotime'])
# names += list(sub.obs_names[list(sortTime)])
names += list(sub.obs_names)
#newNames = adata.obs_names[list(sortTime)]
adataRet = adata[names,:]
return adataRet
Check gland cell trajectories and/or hair cells¶
Save gene count matrices for reading into R¶
In [ ]:
#Nematocytes: 11,12,17,23
#Hair cells: 5,10,21
#Gland cells: 27,25,32,34,22
#Neurons: 31,26,6,9
#i-cells:0
# full_lin = bus_fs_raw[bus_fs_raw.obs['cellRanger_louvain'].isin([5,10,21,0,11,12,17,23,27,25,32,34,22])]
#Not including nematocytes here
full_lin = bus_fs_raw[bus_fs_raw.obs['cellRanger_louvain'].isin([31,26,6,9,0,27,25,32,34,22])]
full_lin
Out[ ]:
View of AnnData object with n_obs × n_vars = 3296 × 46716
obs: 'batch', 'cellRanger_louvain', 'fed', 'orgID', 'annos', 'annosSub'
uns: 'annos_colors', 'cellRanger_louvain_colors'
In [ ]:
io.mmwrite('allLineage.mtx', full_lin.X)
In [ ]:
meta = pd.DataFrame()
meta['cluster'] = full_lin.obs['cellRanger_louvain']
meta['name'] = full_lin.obs['annosSub']
meta['fed'] = full_lin.obs['fed']
meta.head()
Out[ ]:
| cluster | name | fed | |
|---|---|---|---|
| index | |||
| AAACCTGCAAAGTGCG-1 | 6 | Developing Neural Cells | False |
| AAACCTGCAATCACAC-1 | 9 | Neural Cells-A | False |
| AAACCTGGTGCAGTAG-1 | 9 | Neural Cells-A | True |
| AAACCTGTCGGATGGA-1 | 0 | i-Cells | True |
| AAACGGGAGTGAACAT-1 | 6 | Developing Neural Cells | True |
In [ ]:
meta.to_csv('allLinMeta.csv')
In [ ]:
genes = pd.DataFrame()
genes['genes'] = full_lin.var_names
genes.to_csv('genes.csv',index=None)
Lineage with Neurons
In [ ]:
#Check all cells + neurons (31,26,6,9)
neur_lin = bus_fs_raw[bus_fs_raw.obs['cellRanger_louvain'].isin([31,26,6,9,0,27,25,32,34,22,5,10,21,11,12,17,23])]
neur_lin
#io.mmwrite('neurLineage.mtx', neur_lin.X)
Out[ ]:
View of AnnData object with n_obs × n_vars = 6115 × 46716
obs: 'batch', 'cellRanger_louvain', 'fed', 'orgID', 'annos', 'annosSub'
uns: 'annos_colors', 'cellRanger_louvain_colors'
In [ ]:
io.mmwrite('neurLineage.mtx', neur_lin.X)
meta = pd.DataFrame()
meta['cluster'] = neur_lin.obs['cellRanger_louvain']
meta['name'] = neur_lin.obs['annosSub']
meta['fed'] = neur_lin.obs['fed']
meta.to_csv('neurLinMeta.csv')
In [ ]:
genes = pd.DataFrame()
genes['genes'] = neur_lin.var_names
genes.to_csv('neurGenes.csv',index=None)
Control test with outgroup i-cell derived cell type (medium oocytes, 2)
In [ ]:
#Check all cells + neurons (31,26,6,9)
control_lin = bus_fs_raw[bus_fs_raw.obs['cellRanger_louvain'].isin([31,26,6,9,0,27,25,32,34,22,5,10,21,11,12,17,23,2])]
control_lin
io.mmwrite('controlLineage.mtx', control_lin.X)
meta = pd.DataFrame()
meta['cluster'] = control_lin.obs['cellRanger_louvain']
meta['name'] = control_lin.obs['annosSub']
meta['fed'] = control_lin.obs['fed']
meta.to_csv('controlLinMeta.csv')
In [ ]:
genes = pd.DataFrame()
genes['genes'] = control_lin.var_names
genes.to_csv('controlGenes.csv',index=None)
R URD Analysis¶
In [ ]:
# %%R
# #Set up R environment
# install.packages("BiocManager")
# BiocManager::install(version = "3.10")
In [ ]:
!sudo apt-get update
!sudo apt-get install libxml2-dev
!sudo apt-get install r-cran-xml
!sudo apt-get install libcurl4-openssl-dev
Get:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B] Ign:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease Get:3 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB] Get:4 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ Packages [60.9 kB] Hit:5 http://archive.ubuntu.com/ubuntu bionic InRelease Get:6 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB] Ign:7 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 InRelease Get:8 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Release [697 B] Hit:9 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 Release Get:10 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Release.gpg [836 B] Get:11 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB] Ign:13 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Packages Get:13 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Packages [798 kB] Hit:14 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease Get:15 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,413 kB] Get:16 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB] Hit:17 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease Get:18 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [452 kB] Get:19 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease [21.3 kB] Get:20 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [423 kB] Get:21 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,153 kB] Get:22 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,184 kB] Get:23 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,770 kB] Get:24 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [2,585 kB] Get:25 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [906 kB] Get:26 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 Packages [41.5 kB] Fetched 13.1 MB in 7s (1,838 kB/s) Reading package lists... Done Reading package lists... Done Building dependency tree Reading state information... Done libxml2-dev is already the newest version (2.9.4+dfsg1-6.1ubuntu1.3). The following package was automatically installed and is no longer required: libnvidia-common-460 Use 'sudo apt autoremove' to remove it. 0 upgraded, 0 newly installed, 0 to remove and 95 not upgraded. Reading package lists... Done Building dependency tree Reading state information... Done The following package was automatically installed and is no longer required: libnvidia-common-460 Use 'sudo apt autoremove' to remove it. The following NEW packages will be installed: r-cran-xml 0 upgraded, 1 newly installed, 0 to remove and 95 not upgraded. Need to get 1,708 kB of archives. After this operation, 3,073 kB of additional disk space will be used. Get:1 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 r-cran-xml amd64 3.99-0.6-1cran1.1804.0 [1,708 kB] Fetched 1,708 kB in 2s (700 kB/s) debconf: unable to initialize frontend: Dialog debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.) debconf: falling back to frontend: Readline debconf: unable to initialize frontend: Readline debconf: (This frontend requires a controlling tty.) debconf: falling back to frontend: Teletype dpkg-preconfigure: unable to re-open stdin: Selecting previously unselected package r-cran-xml. (Reading database ... 160706 files and directories currently installed.) Preparing to unpack .../r-cran-xml_3.99-0.6-1cran1.1804.0_amd64.deb ... Unpacking r-cran-xml (3.99-0.6-1cran1.1804.0) ... Setting up r-cran-xml (3.99-0.6-1cran1.1804.0) ... Reading package lists... Done Building dependency tree Reading state information... Done libcurl4-openssl-dev is already the newest version (7.58.0-2ubuntu3.13). The following package was automatically installed and is no longer required: libnvidia-common-460 Use 'sudo apt autoremove' to remove it. 0 upgraded, 0 newly installed, 0 to remove and 95 not upgraded.
In [ ]:
%%R
install.packages("rgl",repos = "http://cran.us.r-project.org")
# Load packages
suppressPackageStartupMessages(library(rgl))
R[write to console]: Installing package into ‘/usr/local/lib/R/site-library’ (as ‘lib’ is unspecified) R[write to console]: also installing the dependencies ‘sass’, ‘jquerylib’, ‘httpuv’, ‘xtable’, ‘sourcetools’, ‘later’, ‘promises’, ‘bslib’, ‘lazyeval’, ‘miniUI’, ‘webshot’, ‘shinyjs’, ‘htmlwidgets’, ‘shiny’, ‘crosstalk’, ‘manipulateWidget’ R[write to console]: trying URL 'http://cran.us.r-project.org/src/contrib/sass_0.4.0.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 3022459 bytes (2.9 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: 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console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 1.7 MB R[write to console]: trying URL 'http://cran.us.r-project.org/src/contrib/xtable_1.8-4.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 564589 bytes (551 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to 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to console]: trying URL 'http://cran.us.r-project.org/src/contrib/sourcetools_0.1.7.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 24155 bytes (23 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = 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R[write to console]: R[write to console]: downloaded 81 KB R[write to console]: trying URL 'http://cran.us.r-project.org/src/contrib/miniUI_0.1.1.1.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 97958 bytes (95 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = 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console]: Content type 'application/x-gzip' R[write to console]: length 1043727 bytes (1019 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write 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R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to 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trying URL 'http://cran.us.r-project.org/src/contrib/rgl_0.106.8.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 3061452 bytes (2.9 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to 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In [ ]:
%%R
if (as.numeric(R.version$major) <= 3 && as.numeric(R.version$minor) < 5) {
# R < 3.5
message("Loading Bioconductor (biocLite) to install required packages.")
source("https://bioconductor.org/biocLite.R")
message("Installing required packages from Bioconductor.")
biocLite(c('Biobase', 'S4Vectors', 'AnnotationDbi', 'destiny'), suppressUpdates=T)
} else {
# R >= 3.5
if (!requireNamespace("BiocManager", quietly = TRUE)) {
message("Installing biocManager to install required Bioconductor packages.")
install.packages("BiocManager")
}
message("Installing required packages from Bioconductor (BiocManager).")
BiocManager::install(c('Biobase', 'S4Vectors', 'AnnotationDbi', 'destiny'), suppressUpdates=T)
}
# Check that Bioconductor installation went smoothly.
if (!requireNamespace("Biobase", quietly = TRUE)) {stop("Failed to install required package 'Biobase' from Bioconductor.")}
if (!requireNamespace("S4Vectors", quietly = TRUE)) {stop("Failed to install required package 'S4Vectors' from Bioconductor.")}
if (!requireNamespace("AnnotationDbi", quietly = TRUE)) {stop("Failed to install required package 'AnnotationDbi' from Bioconductor.")}
if (!requireNamespace("destiny", quietly = TRUE)) {stop("Failed to install required package 'destiny' from Bioconductor.")}
# Install URD (and devtools, if necessary)
if (requireNamespace("devtools", quietly = TRUE)) {
message("Installing URD")
devtools::install_github(repo="farrellja/URD")
} else {
message("Installing devtools")
install.packages("devtools")
message("Installing URD")
devtools::install_github(repo="farrellja/URD")
}
# Check that URD installed.
if (requireNamespace("URD", quietly = TRUE)) {
message("URD installed successfully!")
message('You can load it by typing: library("URD")')
message('Try "?URD" for starting tips.')
} else {
message("Something went wrong. It doesn't seem that URD installed correctly.")
}
R[write to console]: Installing required packages from Bioconductor (BiocManager).
R[write to console]: 'getOption("repos")' replaces Bioconductor standard repositories, see
'?repositories' for details
replacement repositories:
CRAN: https://cran.rstudio.com
R[write to console]: Bioconductor version 3.12 (BiocManager 1.30.15), R 4.0.5 (2021-03-31)
R[write to console]: Old packages: 'IRkernel', 'cachem', 'devtools', 'dplyr', 'fansi', 'hms',
'openssl', 'pillar', 'rmarkdown', 'rversions', 'stringi', 'systemfonts',
'tibble', 'tinytex', 'xfun', 'zip', 'Matrix', 'mgcv'
Update all/some/none? [a/s/n]: a
R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/IRkernel_1.2.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 62663 bytes (61 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 61 KB R[write to console]: R[write to console]: R[write to console]: The downloaded source packages are in ‘/tmp/RtmpMYCr4q/downloaded_packages’ R[write to console]: R[write to console]: R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/cachem_1.0.5.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 24974 bytes (24 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to 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to console]: trying URL 'https://cran.rstudio.com/src/contrib/devtools_2.4.1.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 370717 bytes (362 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 362 KB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/dplyr_1.0.6.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 954401 bytes (932 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write 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(294 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 294 KB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/hms_1.1.0.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 43142 bytes (42 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 42 KB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/openssl_1.4.4.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 1311285 bytes (1.3 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to 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R[write to console]: downloaded 1.3 MB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/pillar_1.6.1.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 1111747 bytes (1.1 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 1.1 MB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/rmarkdown_2.8.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 3244734 bytes (3.1 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = 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'application/x-gzip' R[write to console]: length 42064 bytes (41 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 41 KB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/stringi_1.6.2.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 7575370 bytes (7.2 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 7.2 MB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/systemfonts_1.0.2.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 80693 bytes (78 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 78 KB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/tibble_3.1.2.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 686145 bytes (670 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 670 KB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/tinytex_0.32.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 30014 bytes (29 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to 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'https://cran.rstudio.com/src/contrib/xfun_0.23.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 111696 bytes (109 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 109 KB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/zip_2.2.0.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 114391 bytes (111 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 111 KB R[write to console]: R[write to console]: R[write to console]: The downloaded source packages are in ‘/tmp/RtmpMYCr4q/downloaded_packages’ R[write to console]: R[write to console]: R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/Matrix_1.3-4.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 2050384 bytes (2.0 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 2.0 MB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/mgcv_1.8-36.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 1207096 bytes (1.2 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 1.2 MB R[write to console]: R[write to console]: R[write to console]: The downloaded source packages are in ‘/tmp/RtmpMYCr4q/downloaded_packages’ R[write to console]: R[write to console]: R[write to console]: Updating HTML index of packages in '.Library' R[write to console]: Installing URD R[write to console]: Downloading GitHub repo farrellja/URD@HEAD R[write to console]: Skipping 1 packages not available: destiny R[write to console]: Installing 22 packages: caTools, gtools, igraph, gridExtra, polyclip, tweenr, gplots, plyr, gdata, graphlayouts, tidygraph, viridis, ggrepel, ggforce, Rtsne, ROCR, reshape2, RANN, minpack.lm, gmodels, ggraph, diptest R[write to console]: Installing packages into ‘/usr/local/lib/R/site-library’ (as ‘lib’ is unspecified) R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/caTools_1.18.2.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 64960 bytes (63 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to 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'https://cran.rstudio.com/src/contrib/gtools_3.8.2.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 197529 bytes (192 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write 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to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 77 KB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/tweenr_1.0.2.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 568677 bytes (555 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to 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downloaded 555 KB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/gplots_3.1.1.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 520248 bytes (508 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = 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length 1058430 bytes (1.0 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: R[write to console]: downloaded 1.0 MB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/graphlayouts_0.7.1.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 2410184 bytes (2.3 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = 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console]: = R[write to console]: R[write to console]: downloaded 114 KB R[write to console]: trying URL 'https://cran.rstudio.com/src/contrib/viridis_0.6.1.tar.gz' R[write to console]: Content type 'application/x-gzip' R[write to console]: length 3054905 bytes (2.9 MB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to 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to console]: Content type 'application/x-gzip' R[write to console]: length 858093 bytes (837 KB) R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = R[write to console]: = 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R[write to console]: Installing package into ‘/usr/local/lib/R/site-library’
(as ‘lib’ is unspecified)
R[write to console]: Registered S3 method overwritten by 'gplots':
method from
reorder.factor gdata
R[write to console]: URD installed successfully!
R[write to console]: You can load it by typing: library("URD")
R[write to console]: Try "?URD" for starting tips.
In [ ]:
%%R
suppressPackageStartupMessages(library(URD))
#install.packages("Matrix")
suppressPackageStartupMessages(library(Matrix))
No Nematocyte Lineage (Gland cells and Neural Cells)¶
In [ ]:
%%R
fullDF <- t(as.matrix(readMM("allLineage.mtx")))
genes <- read.table("genes.csv", header = TRUE, sep = ",")
meta <- read.table("allLinMeta.csv", header = TRUE, sep = ",")
rownames(fullDF) <- genes$genes
colnames(fullDF) <- meta$index
rownames(meta) <- meta$index
head(meta)
index cluster name fed AAACCTGCAAAGTGCG-1 AAACCTGCAAAGTGCG-1 6 Developing Neural Cells False AAACCTGCAATCACAC-1 AAACCTGCAATCACAC-1 9 Neural Cells-A False AAACCTGGTGCAGTAG-1 AAACCTGGTGCAGTAG-1 9 Neural Cells-A True AAACCTGTCGGATGGA-1 AAACCTGTCGGATGGA-1 0 i-Cells True AAACGGGAGTGAACAT-1 AAACGGGAGTGAACAT-1 6 Developing Neural Cells True AAACGGGCAAACCCAT-1 AAACGGGCAAACCCAT-1 0 i-Cells False
In [ ]:
%%R
#Test removing unknown cluster 17
subMeta <- meta[meta$cluster != 17,]
subDF <- fullDF[,subMeta$index]
head(subMeta)
index cluster name fed AAACCTGCAAAGTGCG-1 AAACCTGCAAAGTGCG-1 6 Developing Neural Cells False AAACCTGCAATCACAC-1 AAACCTGCAATCACAC-1 9 Neural Cells-A False AAACCTGGTGCAGTAG-1 AAACCTGGTGCAGTAG-1 9 Neural Cells-A True AAACCTGTCGGATGGA-1 AAACCTGTCGGATGGA-1 0 i-Cells True AAACGGGAGTGAACAT-1 AAACGGGAGTGAACAT-1 6 Developing Neural Cells True AAACGGGCAAACCCAT-1 AAACGGGCAAACCCAT-1 0 i-Cells False
In [ ]:
%%R
knitr::opts_chunk$set(echo = TRUE)
rgl::setupKnitr()
In [ ]:
%%R
# Create an URD object, which will filter the data, then normalize and log-transform it.
lin <- createURD(count.data = fullDF, meta = meta, min.cells=3, min.counts=3)
lin
R[write to console]: 2021-06-01 16:46:16: Filtering cells by number of genes. R[write to console]: 2021-06-01 16:46:27: Filtering genes by number of cells. R[write to console]: 2021-06-01 16:46:36: Filtering genes by number of counts across entire data. R[write to console]: 2021-06-01 16:46:42: Filtering genes by maximum observed expression. R[write to console]: 2021-06-01 16:46:49: Creating URD object. R[write to console]: 2021-06-01 16:46:50: Determining normalization factors. R[write to console]: 2021-06-01 16:46:52: Normalizing and log-transforming the data. R[write to console]: 2021-06-01 16:46:58: Finishing setup of the URD object. R[write to console]: 2021-06-01 16:46:59: All done.
URD object: 15514 genes x 2518 cells.
In [ ]:
%%R
# Copy stage from @meta to @group.ids
lin@group.ids$stage <- as.character(lin@meta[rownames(lin@group.ids),"cluster"])
# Get variable genes for each group of stages
# diffCV.cutoff can be varied to include more or fewer genes.
stages <- sort(unique(lin@group.ids$stage))
var.by.stage <- lapply(seq(length(stages)), function(n) {
findVariableGenes(lin, cells.fit=cellsInCluster(lin, "stage", stages[n]), set.object.var.genes=F, diffCV.cutoff=0.3, mean.min=.005, mean.max=100, main.use=paste0("Stages ", stages[n]), do.plot=T)
})
In [ ]:
%%R
# Combine the results from each group of stages into a single list of variable genes and load into the URD object
var.genes <- sort(unique(unlist(var.by.stage)))
lin@var.genes <- var.genes
In [ ]:
%%R
# Calculate PCA and consider those PCs that with standard deviation 2x expected by noise as significant
lin <- calcPCA(lin, mp.factor = 2)
pcSDPlot(lin)
# Calculate tSNE
set.seed(42)
lin <- calcTsne(object = lin)
plotDim(lin, "name", plot.title = "tSNE: Cell Type")
plotDim(lin, "stage", plot.title = "tSNE: Cluster")
[1] "2021-06-01 16:47:09: Centering and scaling data." [1] "2021-06-01 16:47:11: Removing genes with no variation." [1] "2021-06-01 16:47:11: Calculating PCA." [1] "2021-06-01 16:47:41: Estimating significant PCs." [1] "Marchenko-Pastur eigenvalue null upper bound: 5.34930159805564" [1] "28 PCs have eigenvalues larger than 2 times null upper bound." [1] "Storing 56 PCs."
In [ ]:
%%R
# In this case, knn=100 (larger than sqrt(n.cells)) works well because there are not many cell types.
# Sigma 16 is slightly smaller than the sigma auto-determined by using NULL parameter.
lin <- calcDM(lin, knn = 60)
# Here we use all cells from the first stage as the root
root.cells <- cellsInCluster(lin, "stage", "0")
# Then we run 'flood' simulations
lin.floods <- floodPseudotime(lin, root.cells = root.cells, n=50, minimum.cells.flooded = 2, verbose=F)
# The we process the simulations into a pseudotime
lin <- floodPseudotimeProcess(lin, lin.floods, floods.name="pseudotime")
pseudotimePlotStabilityOverall(lin)
plotDists(lin, "pseudotime", "stage", plot.title="Pseudotime by stage")
[1] "destiny determined an optimal global sigma of 25.893"
In [ ]:
%%R
sort(unique(lin@group.ids$stage))
[1] "0" "22" "25" "26" "27" "31" "32" "34" "6" "9"
In [ ]:
%%R
# Create a subsetted object of just those cells from the final stage
lin.tips <- urdSubset(lin, cells.keep=cellsInCluster(lin, "stage", c(9,26,31,27,25,32,34,22)))
# Use the variable genes that were calculated only on the final group of stages (which
# contain the last stage).
#lin.tips@var.genes <- sort(unique(unlist(var.by.stage[c(2,3,4,5,6,7,8,10)])))
# Calculate PCA and tSNE
lin.tips <- calcPCA(lin.tips, mp.factor = 1.5)
pcSDPlot(lin.tips)
[1] "2021-06-01 17:06:38: Centering and scaling data." [1] "2021-06-01 17:06:38: Removing genes with no variation." [1] "2021-06-01 17:06:39: Calculating PCA." [1] "2021-06-01 17:06:41: Estimating significant PCs." [1] "Marchenko-Pastur eigenvalue null upper bound: 11.0838190618371" [1] "25 PCs have eigenvalues larger than 1.5 times null upper bound." [1] "Storing 50 PCs."
In [ ]:
%%R
# Calculate graph clustering of these cells
lin.tips <- graphClustering(lin.tips, num.nn = 50, do.jaccard=T, method="Louvain")
# Copy cluster identities from axial.6somite object to a new clustering ("tip.clusters") in the full axial object.
lin@group.ids[rownames(lin.tips@group.ids), "tip.clusters"] <- lin.tips@group.ids$stage
# Determine the parameters of the logistic used to bias the transition probabilities. The procedure
# is relatively robust to this parameter, but the cell numbers may need to be modified for larger
# or smaller data sets.
lin.ptlogistic <- pseudotimeDetermineLogistic(lin, "pseudotime", optimal.cells.forward=0, max.cells.back=40, do.plot = T)
[1] "Mean pseudotime back (~40 cells) 0.0106395048641373" [1] "Chance of accepted move to equal pseudotime is 0.99009900990099" [1] "Mean pseudotime forward (~0 cells) 0"
In [ ]:
%%R
# Bias the transition matrix acording to pseudotime
lin.biased.tm <- as.matrix(pseudotimeWeightTransitionMatrix(lin, "pseudotime", logistic.params=lin.ptlogistic))
# Simulate the biased random walks from each tip
lin.walks <- simulateRandomWalksFromTips(lin, tip.group.id="tip.clusters", root.cells=root.cells, transition.matrix = lin.biased.tm, n.per.tip = 25000, root.visits = 1, max.steps = 5000, verbose = F)
# Process the biased random walks into visitation frequencies
lin <- processRandomWalksFromTips(lin, lin.walks, verbose = F)
#Plot tips
plotDim(lin, "tip.clusters", plot.title="Cells in each tip")
In [ ]:
%%R
plotDim(lin, "name", plot.title="Cells in each tip")
In [ ]:
%%R
#Plot visitation frequencies from tips
plotDim(lin, "visitfreq.log.25", plot.title="Visitation frequency from tip 9 (log10)", transitions.plot=10000)
In [ ]:
%%R
# Load the cells used for each tip into the URD object
lin.tree <- loadTipCells(lin, "tip.clusters")
# # Combine the two small clusters in a single population into one tip
# lin.tree <- combineTipVisitation(lin.tree, "16", "5", new.tip = "16")
# Build the tree, c(10,21,17,23,27,25,32,34,22) = tips
lin.tree <- buildTree(lin.tree, pseudotime = "pseudotime", tips.use=c("9","26","31","27","25","32","22") , divergence.method = "preference", cells.per.pseudotime.bin = 15, bins.per.pseudotime.window = 10, save.all.breakpoint.info = T, p.thresh=0.01,min.cells.per.segment=10)
[1] "Calculating divergence between 9 and 26 (Pseudotime 0 to 0.462)" [1] "Calculating divergence between 9 and 31 (Pseudotime 0 to 0.468)" [1] "Calculating divergence between 9 and 27 (Pseudotime 0 to 0.468)"
R[write to console]: Difference between 9 and 27 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 9 and 25 (Pseudotime 0 to 0.468)" [1] "Calculating divergence between 9 and 32 (Pseudotime 0 to 0.468)" [1] "Calculating divergence between 9 and 22 (Pseudotime 0 to 0.468)" [1] "Calculating divergence between 26 and 31 (Pseudotime 0 to 0.462)" [1] "Calculating divergence between 26 and 27 (Pseudotime 0 to 0.462)"
R[write to console]: Difference between 26 and 27 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 26 and 25 (Pseudotime 0 to 0.462)" [1] "Calculating divergence between 26 and 32 (Pseudotime 0 to 0.462)" [1] "Calculating divergence between 26 and 22 (Pseudotime 0 to 0.462)" [1] "Calculating divergence between 31 and 27 (Pseudotime 0 to 0.502)"
R[write to console]: Difference between 31 and 27 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 31 and 25 (Pseudotime 0 to 0.502)" [1] "Calculating divergence between 31 and 32 (Pseudotime 0 to 0.502)" [1] "Calculating divergence between 31 and 22 (Pseudotime 0 to 0.502)" [1] "Calculating divergence between 27 and 25 (Pseudotime 0 to 0.574)"
R[write to console]: Difference between 27 and 25 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 27 and 32 (Pseudotime 0 to 0.644)" [1] "Calculating divergence between 27 and 22 (Pseudotime 0 to 0.644)" [1] "Calculating divergence between 25 and 32 (Pseudotime 0 to 0.574)" [1] "Calculating divergence between 25 and 22 (Pseudotime 0 to 0.574)" [1] "Calculating divergence between 32 and 22 (Pseudotime 0 to 0.653)" [1] "Joining segments 27 and 22 at pseudotime 0.44 to create segment 33" [1] "Calculating divergence between 9 and 33 (Pseudotime 0 to 0.44)" [1] "Calculating divergence between 26 and 33 (Pseudotime 0 to 0.44)" [1] "Calculating divergence between 31 and 33 (Pseudotime 0 to 0.44)" [1] "Calculating divergence between 25 and 33 (Pseudotime 0 to 0.44)" [1] "Calculating divergence between 32 and 33 (Pseudotime 0 to 0.44)"
R[write to console]: Difference between 32 and 33 always FALSE -- setting breakpoint to end.
[1] "Joining segments 32 and 33 at pseudotime 0.438 to create segment 34" [1] "Calculating divergence between 9 and 34 (Pseudotime 0 to 0.438)" [1] "Calculating divergence between 26 and 34 (Pseudotime 0 to 0.438)" [1] "Calculating divergence between 31 and 34 (Pseudotime 0 to 0.438)" [1] "Calculating divergence between 25 and 34 (Pseudotime 0 to 0.438)" [1] "Joining segments 26 and 31 at pseudotime 0.263 to create segment 35" [1] "Calculating divergence between 9 and 35 (Pseudotime 0 to 0.263)" [1] "Calculating divergence between 25 and 35 (Pseudotime 0 to 0.263)" [1] "Calculating divergence between 34 and 35 (Pseudotime 0 to 0.263)" [1] "Joining segments 9 and 25 at pseudotime 0.216 to create segment 36" [1] "Calculating divergence between 34 and 36 (Pseudotime 0 to 0.216)" [1] "Calculating divergence between 35 and 36 (Pseudotime 0 to 0.216)" [1] "Joining segments 35 and 36 at pseudotime 0.182 to create segment 37" [1] "Calculating divergence between 34 and 37 (Pseudotime 0 to 0.182)" [1] "Joining segments 34 and 37 at pseudotime 0 to create segment 38" [1] "Assigning cells to segments." [1] "Collapsing short segments." [1] "Removing singleton segments." [1] "Reassigning cells to segments." [1] "Assigning cells to nodes." [1] "Laying out tree." [1] "Adding cells to tree."
In [ ]:
# %%R
# #c("10","21","17","23","27","25","32","34","22")
# plotTree(lin.tree, "stage", title="Cell Types")
In [ ]:
%%R
#c("9","26","31","27","25","32","22")
plotTree(lin.tree, "stage", label.segments=T, title="Cell Types")
In [ ]:
#'XLOC_001934', 'XLOC_006720', 'XLOC_031548', HMG-box
# Myb-like 'XLOC_002947'
%%R
#c("9","26","31","27","25","32","22")
plotTree(lin.tree, 'XLOC_002947', title='XLOC_002947 Myb')
R[write to console]: Error in data.for.plot(object, label = label, label.type = label.type, : Cannot find XLOC_002947 in metadata, group.ids, signatures, genes, NMF modules, PCA, or pseudotime. R[write to console]: In addition: R[write to console]: Warning messages: R[write to console]: 1: R[write to console]: In assignCellsToSegments(object, pseudotime, verbose) : R[write to console]: R[write to console]: 422 cells were not visited by a branch that exists at their pseudotime and were not assigned. R[write to console]: 2: R[write to console]: In assignCellsToSegments(object, pseudotime, verbose) : R[write to console]: R[write to console]: 422 cells were not visited by a branch that exists at their pseudotime and were not assigned.
Error in data.for.plot(object, label = label, label.type = label.type, : Cannot find XLOC_002947 in metadata, group.ids, signatures, genes, NMF modules, PCA, or pseudotime.
--------------------------------------------------------------------------- RRuntimeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/rpy2/ipython/rmagic.py in eval(self, code) 267 # Need the newline in case the last line in code is a comment. --> 268 value, visible = ro.r("withVisible({%s\n})" % code) 269 except (ri.embedded.RRuntimeError, ValueError) as exception: /usr/local/lib/python3.7/dist-packages/rpy2/robjects/__init__.py in __call__(self, string) 437 p = rinterface.parse(string) --> 438 res = self.eval(p) 439 return conversion.rpy2py(res) /usr/local/lib/python3.7/dist-packages/rpy2/robjects/functions.py in __call__(self, *args, **kwargs) 198 return (super(SignatureTranslatedFunction, self) --> 199 .__call__(*args, **kwargs)) 200 /usr/local/lib/python3.7/dist-packages/rpy2/robjects/functions.py in __call__(self, *args, **kwargs) 124 new_kwargs[k] = conversion.py2rpy(v) --> 125 res = super(Function, self).__call__(*new_args, **new_kwargs) 126 res = conversion.rpy2py(res) /usr/local/lib/python3.7/dist-packages/rpy2/rinterface_lib/conversion.py in _(*args, **kwargs) 44 def _(*args, **kwargs): ---> 45 cdata = function(*args, **kwargs) 46 # TODO: test cdata is of the expected CType /usr/local/lib/python3.7/dist-packages/rpy2/rinterface.py in __call__(self, *args, **kwargs) 679 if error_occured[0]: --> 680 raise embedded.RRuntimeError(_rinterface._geterrmessage()) 681 return res RRuntimeError: Error in data.for.plot(object, label = label, label.type = label.type, : Cannot find XLOC_002947 in metadata, group.ids, signatures, genes, NMF modules, PCA, or pseudotime. During handling of the above exception, another exception occurred: RInterpreterError Traceback (most recent call last) <ipython-input-48-54ec97d7fd2b> in <module>() ----> 1 get_ipython().run_cell_magic('R', '', '#c("9","26","31","27","25","32","22")\nplotTree(lin.tree, \'XLOC_002947\', title=\'XLOC_002947 Myb\')') /usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell) 2115 magic_arg_s = self.var_expand(line, stack_depth) 2116 with self.builtin_trap: -> 2117 result = fn(magic_arg_s, cell) 2118 return result 2119 <decorator-gen-119> in R(self, line, cell, local_ns) /usr/local/lib/python3.7/dist-packages/IPython/core/magic.py in <lambda>(f, *a, **k) 186 # but it's overkill for just that one bit of state. 187 def magic_deco(arg): --> 188 call = lambda f, *a, **k: f(*a, **k) 189 190 if callable(arg): /usr/local/lib/python3.7/dist-packages/rpy2/ipython/rmagic.py in R(self, line, cell, local_ns) 781 if not e.stdout.endswith(e.err): 782 print(e.err) --> 783 raise e 784 finally: 785 if self.device in ['png', 'svg']: /usr/local/lib/python3.7/dist-packages/rpy2/ipython/rmagic.py in R(self, line, cell, local_ns) 761 return_output = False 762 else: --> 763 text_result, result, visible = self.eval(code) 764 text_output += text_result 765 if visible: /usr/local/lib/python3.7/dist-packages/rpy2/ipython/rmagic.py in eval(self, code) 271 warning_or_other_msg = self.flush() 272 raise RInterpreterError(code, str(exception), --> 273 warning_or_other_msg) 274 text_output = self.flush() 275 return text_output, value, visible[0] RInterpreterError: Failed to parse and evaluate line '#c("9","26","31","27","25","32","22")\nplotTree(lin.tree, \'XLOC_002947\', title=\'XLOC_002947 Myb\')'. R error message: 'Error in data.for.plot(object, label = label, label.type = label.type, : \n Cannot find XLOC_002947 in metadata, group.ids, signatures, genes, NMF modules, PCA, or pseudotime.'
In [ ]:
#'XLOC_001934', 'XLOC_006720', 'XLOC_031548', HMG-box
# Myb-like 'XLOC_002947'
%%R
#c("9","26","31","27","25","32","22")
plotTree(lin.tree, 'XLOC_007085', title='XLOC_007085 Myc3')
In [ ]:
#'XLOC_001934', 'XLOC_006720', 'XLOC_031548', HMG-box
# Myb-like 'XLOC_002947'
%%R
#c("9","26","31","27","25","32","22")
plotTree(lin.tree, 'XLOC_037144', title='XLOC_037144 HvSoxC-like')
In [ ]:
#'XLOC_001934', 'XLOC_006720', 'XLOC_031548', HMG-box
# Myb-like 'XLOC_002947'
%%R
#c("9","26","31","27","25","32","22")
plotTree(lin.tree, 'XLOC_001441', title='XLOC_001441 NDA-1 like')
In [ ]:
#'XLOC_001934', 'XLOC_006720', 'XLOC_031548', HMG-box
# Myb-like 'XLOC_002947'
%%R
#c("9","26","31","27","25","32","22")
plotTree(lin.tree, 'XLOC_019582', title='XLOC_019582 COMA-like')
In [ ]:
In [ ]:
#'XLOC_001934', 'XLOC_006720', 'XLOC_031548', HMG-box
# Myb-like 'XLOC_018971', 'XLOC_029855'
%%R
#c("9","26","31","27","25","32","22")
plotTree(lin.tree, 'XLOC_029855', title='XLOC_029855 Myb-like')
In [ ]:
#'XLOC_001934', 'XLOC_006720', 'XLOC_031548', HMG-box
# Myb-like 'XLOC_018971', 'XLOC_029855'
%%R
#c("9","26","31","27","25","32","22")
plotTree(lin.tree, 'XLOC_031548', title='XLOC_031548 HMG-box')
In [ ]:
#'XLOC_001934', 'XLOC_006720', 'XLOC_031548', HMG-box
# Myb-like 'XLOC_018971', 'XLOC_029855'
%%R
#c("9","26","31","27","25","32","22")
plotTree(lin.tree, 'XLOC_018971', title='XLOC_018971 Myb-like')
In [ ]:
%%R
#c("9","26","31","27","25","32","22")
plotTree(lin.tree, "name", title="Cell Types")
In [ ]:
%%R
tips.to.run <- c("9", "37","25")
Get DE genes along branches
In [ ]:
%%R
# Calculate the markers of each other population.
gene.markers <- list()
for (tipn in 1:length(tips.to.run)) {
tip <- tips.to.run[tipn]
print(paste0(Sys.time(), ": ", tip))
markers <- aucprTestAlongTree(lin.tree, pseudotime="pseudotime", tips=tip, log.effect.size=0.4, auc.factor = 1.25, max.auc.threshold = 0.85, frac.must.express = 0.1, frac.min.diff = 0, root="38", only.return.global=F, must.beat.sibs=0.6, report.debug=T)
gene.markers[[tip]] <- markers
}
[1] "2021-05-19 21:41:37: 9" [1] "2021-05-19 21:43:22: 37" [1] "2021-05-19 21:43:50: 25"
In [ ]:
%%R
gene.markers.de <- lapply(gene.markers, function(x) x[[1]])
gene.markers.stats <- lapply(gene.markers[1:23], function(x) x[[2]])
names(gene.markers.de) <- names(gene.markers)
names(gene.markers.stats) <- names(gene.markers)[1:23]
In [ ]:
%%R
toPlot <- c("9", "37","25")
gene <- list()
segment <- list()
p <- 1
for (c in toPlot){
fname <- paste("20210519_seg", c,"DE_Gland_tips.pdf", sep = "")
pdf(file=fname, width=7.5, height=10)
for (i in rownames(gene.markers.de[[c]])){
plot(plotTree(lin.tree,i, label.segments=T, title=i))
gene[[p]] <- as.vector(i)
segment[[p]] <- as.vector(c)
p <- p+1
}
dev.off()
}
In [ ]:
%%R
deGenesSeg <- data.frame(Genes = unlist(gene), Segment = unlist(segment))
write.csv(deGenesSeg,"deGenesSeg.csv", row.names = FALSE)
Test control tree with outgroup i-cell derived cells¶
In [ ]:
%%R
neurDF <- t(as.matrix(readMM("controlLineage.mtx")))
genes <- read.table("controlGenes.csv", header = TRUE, sep = ",")
neurMeta <- read.table("controlLinMeta.csv", header = TRUE, sep = ",")
rownames(neurDF) <- genes$genes
colnames(neurDF) <- neurMeta$index
rownames(neurMeta) <- neurMeta$index
# Create an URD object, which will filter the data, then normalize and log-transform it.
lin <- createURD(count.data = neurDF, meta = neurMeta, min.cells=1, min.counts=1, max.genes.in.ram = 100)
lin
R[write to console]: 2021-04-23 21:38:10: Filtering cells by number of genes. R[write to console]: 2021-04-23 21:38:29: Filtering genes by number of cells. R[write to console]: 2021-04-23 21:38:50: Filtering genes by number of counts across entire data. R[write to console]: 2021-04-23 21:39:07: Filtering genes by maximum observed expression. R[write to console]: 2021-04-23 21:39:26: Creating URD object. R[write to console]: 2021-04-23 21:39:28: Determining normalization factors. R[write to console]: 2021-04-23 21:39:33: Normalizing and log-transforming the data. R[write to console]: 2021-04-23 21:40:25: Finishing setup of the URD object. R[write to console]: 2021-04-23 21:40:28: All done.
URD object: 21330 genes x 5147 cells.
In [ ]:
%%R
# Copy stage from @meta to @group.ids
lin@group.ids$stage <- as.character(lin@meta[rownames(lin@group.ids),"cluster"])
# Get variable genes for each group of stages
# diffCV.cutoff can be varied to include more or fewer genes.
stages <- sort(unique(lin@group.ids$stage))
var.by.stage <- lapply(seq(length(stages)), function(n) {
findVariableGenes(lin, cells.fit=cellsInCluster(lin, "stage", stages[n]), set.object.var.genes=F, diffCV.cutoff=0.3, mean.min=.005, mean.max=100, main.use=paste0("Stages ", stages[n]), do.plot=F)
})
In [ ]:
%%R
# Combine the results from each group of stages into a single list of variable genes and load into the URD object
var.genes <- sort(unique(unlist(var.by.stage)))
lin@var.genes <- var.genes
In [ ]:
%%R
# Calculate PCA and consider those PCs that with standard deviation 2x expected by noise as significant
lin <- calcPCA(lin, mp.factor = 2)
pcSDPlot(lin)
# Calculate tSNE
set.seed(42)
lin <- calcTsne(object = lin)
plotDim(lin, "stage", plot.title = "tSNE: Cluster")
[1] "2021-04-23 21:41:38: Centering and scaling data." [1] "2021-04-23 21:41:49: Removing genes with no variation." [1] "2021-04-23 21:41:51: Calculating PCA." [1] "2021-04-23 21:44:52: Estimating significant PCs." [1] "Marchenko-Pastur eigenvalue null upper bound: 4.65879528419891" [1] "31 PCs have eigenvalues larger than 2 times null upper bound." [1] "Storing 62 PCs."
In [ ]:
%%R
# In this case, knn=100 (larger than sqrt(n.cells)) works well because there are not many cell types.
# Sigma 16 is slightly smaller than the sigma auto-determined by using NULL parameter.
lin <- calcDM(lin, knn = 60)
# Here we use all cells from the first stage as the root
root.cells <- cellsInCluster(lin, "stage", "0")
# Then we run 'flood' simulations
lin.floods <- floodPseudotime(lin, root.cells = root.cells, n=100, minimum.cells.flooded = 2, verbose=F)
# The we process the simulations into a pseudotime
lin <- floodPseudotimeProcess(lin, lin.floods, floods.name="pseudotime")
pseudotimePlotStabilityOverall(lin)
[1] "destiny determined an optimal global sigma of 27.115"
In [ ]:
%%R
#Check pseudotime assignments per cell type
plotDists(lin, "pseudotime", "stage", plot.title="Pseudotime by stage")
In [ ]:
%%R
# Create a subsetted object of just those cells from the final stage
#Test c(26,31,9,27,25,32,34,22,21, 23) , c(26,31,9,27,25,32,34,22, 23), c(26,31,9,27,25,32,34,22,10, 23)
lin.tips <- urdSubset(lin, cells.keep=cellsInCluster(lin, "stage", c(26,31,9,27,25,32,34,22, 17,10,21, 23,2)))
# Use the variable genes that were calculated only on the final group of stages (which
# contain the last stage).
#lin.tips@var.genes <- var.by.stage[[4]]
# Calculate PCA and tSNE
lin.tips <- calcPCA(lin.tips, mp.factor = 1.5)
pcSDPlot(lin.tips)
[1] "2021-04-23 22:14:02: Centering and scaling data." [1] "2021-04-23 22:14:05: Removing genes with no variation." [1] "2021-04-23 22:14:06: Calculating PCA." [1] "2021-04-23 22:14:29: Estimating significant PCs." [1] "Marchenko-Pastur eigenvalue null upper bound: 8.04989603099451" [1] "28 PCs have eigenvalues larger than 1.5 times null upper bound." [1] "Storing 56 PCs."
In [ ]:
%%R
# Calculate graph clustering of these cells
#lin.tips <- graphClustering(lin.tips, num.nn = 50, do.jaccard=T, method="Louvain")
# Copy cluster identities from axial.6somite object to a new clustering ("tip.clusters") in the full axial object.
lin@group.ids[rownames(lin.tips@group.ids), "tip.clusters"] <- lin.tips@group.ids$stage
# Determine the parameters of the logistic used to bias the transition probabilities. The procedure
# is relatively robust to this parameter, but the cell numbers may need to be modified for larger
# or smaller data sets.
lin.ptlogistic <- pseudotimeDetermineLogistic(lin, "pseudotime", optimal.cells.forward=0, max.cells.back=100, do.plot = T) #20, 40
[1] "Mean pseudotime back (~100 cells) 0.0132731806662228" [1] "Chance of accepted move to equal pseudotime is 0.99009900990099" [1] "Mean pseudotime forward (~0 cells) 0"
In [ ]:
%%R
# Bias the transition matrix acording to pseudotime
lin.biased.tm <- as.matrix(pseudotimeWeightTransitionMatrix(lin, "pseudotime", logistic.params=lin.ptlogistic))
# Simulate the biased random walks from each tip
lin.walks <- simulateRandomWalksFromTips(lin, tip.group.id="tip.clusters", root.cells=root.cells, transition.matrix = lin.biased.tm,n.per.tip = 20000, root.visits = 1, max.steps = 5000, verbose = F)
# Process the biased random walks into visitation frequencies
lin <- processRandomWalksFromTips(lin, lin.walks, verbose = F)
#Plot tips
plotDim(lin, "tip.clusters", plot.title="Cells in each tip")
In [ ]:
%%R
pseudotimePlotStabilityOverall(lin)
In [ ]:
%%R
plotDim(lin, "name", plot.title="Cells in each tip")
In [ ]:
%%R
#Plot visitation frequencies from tips
plotDim(lin, "visitfreq.log.2", plot.title="Visitation frequency from tip 2 (log10)", transitions.plot=10000)
In [ ]:
%%R
# Load the cells used for each tip into the URD object
lin.tree <- loadTipCells(lin, "tip.clusters")
# # Combine the two small clusters in a single population into one tip
# lin.tree <- combineTipVisitation(lin.tree, "16", "5", new.tip = "16")
# Build the tree, c(10,21,17,23,27,25,32,34,22) = tips
#c("26","31","9","27","25","32","22", "23")
#c("26","31","9","27","25","32","22", "23","21")
#c("26","31","9","27","25","32","22", "17","21")
#c("26","31","9","27","25","32","22", "17")
lin.tree <- buildTree(lin.tree, pseudotime = "pseudotime", tips.use=c("26","31","9","27","25","32","22", "23","21","10", "2") , divergence.method = "preference", cells.per.pseudotime.bin = 30, bins.per.pseudotime.window = 8, save.all.breakpoint.info = T, p.thresh=0.005)
[1] "Calculating divergence between 26 and 31 (Pseudotime 0 to 0.414)" [1] "Calculating divergence between 26 and 9 (Pseudotime 0 to 0.414)" [1] "Calculating divergence between 26 and 27 (Pseudotime 0 to 0.414)" [1] "Calculating divergence between 26 and 25 (Pseudotime 0 to 0.414)" [1] "Calculating divergence between 26 and 32 (Pseudotime 0 to 0.414)" [1] "Calculating divergence between 26 and 22 (Pseudotime 0 to 0.414)" [1] "Calculating divergence between 26 and 23 (Pseudotime 0 to 0.414)"
R[write to console]: Difference between 26 and 23 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 26 and 21 (Pseudotime 0 to 0.414)" [1] "Calculating divergence between 26 and 10 (Pseudotime 0 to 0.414)" [1] "Calculating divergence between 26 and 2 (Pseudotime 0 to 0.414)"
R[write to console]: Difference between 26 and 2 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 31 and 9 (Pseudotime 0 to 0.432)" [1] "Calculating divergence between 31 and 27 (Pseudotime 0 to 0.432)" [1] "Calculating divergence between 31 and 25 (Pseudotime 0 to 0.429)" [1] "Calculating divergence between 31 and 32 (Pseudotime 0 to 0.432)" [1] "Calculating divergence between 31 and 22 (Pseudotime 0 to 0.432)" [1] "Calculating divergence between 31 and 23 (Pseudotime 0 to 0.432)"
R[write to console]: Difference between 31 and 23 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 31 and 21 (Pseudotime 0 to 0.432)" [1] "Calculating divergence between 31 and 10 (Pseudotime 0 to 0.432)" [1] "Calculating divergence between 31 and 2 (Pseudotime 0 to 0.432)"
R[write to console]: Difference between 31 and 2 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 9 and 27 (Pseudotime 0 to 0.452)" [1] "Calculating divergence between 9 and 25 (Pseudotime 0 to 0.429)" [1] "Calculating divergence between 9 and 32 (Pseudotime 0 to 0.452)" [1] "Calculating divergence between 9 and 22 (Pseudotime 0 to 0.452)" [1] "Calculating divergence between 9 and 23 (Pseudotime 0 to 0.452)"
R[write to console]: Difference between 9 and 23 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 9 and 21 (Pseudotime 0 to 0.452)" [1] "Calculating divergence between 9 and 10 (Pseudotime 0 to 0.452)" [1] "Calculating divergence between 9 and 2 (Pseudotime 0 to 0.445)"
R[write to console]: Difference between 9 and 2 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 27 and 25 (Pseudotime 0 to 0.429)" [1] "Calculating divergence between 27 and 32 (Pseudotime 0 to 0.506)" [1] "Calculating divergence between 27 and 22 (Pseudotime 0 to 0.509)" [1] "Calculating divergence between 27 and 23 (Pseudotime 0 to 0.509)"
R[write to console]: Difference between 27 and 23 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 27 and 21 (Pseudotime 0 to 0.509)" [1] "Calculating divergence between 27 and 10 (Pseudotime 0 to 0.509)"
R[write to console]: Difference between 27 and 10 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 27 and 2 (Pseudotime 0 to 0.445)"
R[write to console]: Difference between 27 and 2 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 25 and 32 (Pseudotime 0 to 0.429)" [1] "Calculating divergence between 25 and 22 (Pseudotime 0 to 0.429)" [1] "Calculating divergence between 25 and 23 (Pseudotime 0 to 0.429)"
R[write to console]: Difference between 25 and 23 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 25 and 21 (Pseudotime 0 to 0.429)" [1] "Calculating divergence between 25 and 10 (Pseudotime 0 to 0.429)"
R[write to console]: Difference between 25 and 10 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 25 and 2 (Pseudotime 0 to 0.429)"
R[write to console]: Difference between 25 and 2 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 32 and 22 (Pseudotime 0 to 0.506)" [1] "Calculating divergence between 32 and 23 (Pseudotime 0 to 0.506)"
R[write to console]: Difference between 32 and 23 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 32 and 21 (Pseudotime 0 to 0.506)" [1] "Calculating divergence between 32 and 10 (Pseudotime 0 to 0.506)"
R[write to console]: Difference between 32 and 10 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 32 and 2 (Pseudotime 0 to 0.445)"
R[write to console]: Difference between 32 and 2 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 22 and 23 (Pseudotime 0 to 0.531)" [1] "Calculating divergence between 22 and 21 (Pseudotime 0 to 0.531)" [1] "Calculating divergence between 22 and 10 (Pseudotime 0 to 0.531)"
R[write to console]: Difference between 22 and 10 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 22 and 2 (Pseudotime 0 to 0.445)"
R[write to console]: Difference between 22 and 2 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 23 and 21 (Pseudotime 0 to 0.552)" [1] "Calculating divergence between 23 and 10 (Pseudotime 0 to 0.535)" [1] "Calculating divergence between 23 and 2 (Pseudotime 0 to 0.445)"
R[write to console]: Difference between 23 and 2 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 21 and 10 (Pseudotime 0 to 0.535)" [1] "Calculating divergence between 21 and 2 (Pseudotime 0 to 0.445)"
R[write to console]: Difference between 21 and 2 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 10 and 2 (Pseudotime 0 to 0.445)"
R[write to console]: Difference between 10 and 2 always TRUE -- setting breakpoint to beginning.
[1] "Joining segments 27 and 32 at pseudotime 0.385 to create segment 33" [1] "Calculating divergence between 26 and 33 (Pseudotime 0 to 0.385)" [1] "Calculating divergence between 31 and 33 (Pseudotime 0 to 0.385)" [1] "Calculating divergence between 9 and 33 (Pseudotime 0 to 0.385)" [1] "Calculating divergence between 25 and 33 (Pseudotime 0 to 0.385)" [1] "Calculating divergence between 22 and 33 (Pseudotime 0 to 0.385)"
R[write to console]: Difference between 22 and 33 always FALSE -- setting breakpoint to end.
[1] "Calculating divergence between 23 and 33 (Pseudotime 0 to 0.385)"
R[write to console]: Difference between 23 and 33 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 21 and 33 (Pseudotime 0 to 0.385)" [1] "Calculating divergence between 10 and 33 (Pseudotime 0 to 0.385)"
R[write to console]: Difference between 10 and 33 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 2 and 33 (Pseudotime 0 to 0.385)"
R[write to console]: Difference between 2 and 33 always TRUE -- setting breakpoint to beginning.
[1] "Joining segments 22 and 33 at pseudotime 0.385 to create segment 34" [1] "Calculating divergence between 26 and 34 (Pseudotime 0 to 0.385)" [1] "Calculating divergence between 31 and 34 (Pseudotime 0 to 0.385)" [1] "Calculating divergence between 9 and 34 (Pseudotime 0 to 0.385)" [1] "Calculating divergence between 25 and 34 (Pseudotime 0 to 0.385)" [1] "Calculating divergence between 23 and 34 (Pseudotime 0 to 0.385)"
R[write to console]: Difference between 23 and 34 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 21 and 34 (Pseudotime 0 to 0.385)" [1] "Calculating divergence between 10 and 34 (Pseudotime 0 to 0.385)"
R[write to console]: Difference between 10 and 34 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 2 and 34 (Pseudotime 0 to 0.385)"
R[write to console]: Difference between 2 and 34 always TRUE -- setting breakpoint to beginning.
[1] "Joining segments 21 and 10 at pseudotime 0.284 to create segment 35" [1] "Calculating divergence between 26 and 35 (Pseudotime 0 to 0.284)" [1] "Calculating divergence between 31 and 35 (Pseudotime 0 to 0.284)" [1] "Calculating divergence between 9 and 35 (Pseudotime 0 to 0.284)" [1] "Calculating divergence between 25 and 35 (Pseudotime 0 to 0.284)"
R[write to console]: Difference between 25 and 35 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 23 and 35 (Pseudotime 0 to 0.284)" [1] "Calculating divergence between 2 and 35 (Pseudotime 0 to 0.284)"
R[write to console]: Difference between 2 and 35 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 34 and 35 (Pseudotime 0 to 0.284)"
R[write to console]: Difference between 34 and 35 always TRUE -- setting breakpoint to beginning.
[1] "Joining segments 9 and 25 at pseudotime 0.16 to create segment 36" [1] "Calculating divergence between 26 and 36 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 26 and 36 always FALSE -- setting breakpoint to end.
[1] "Calculating divergence between 31 and 36 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 31 and 36 always FALSE -- setting breakpoint to end.
[1] "Calculating divergence between 23 and 36 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 23 and 36 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 2 and 36 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 2 and 36 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 34 and 36 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 34 and 36 always FALSE -- setting breakpoint to end.
[1] "Calculating divergence between 35 and 36 (Pseudotime 0 to 0.16)" [1] "Joining segments 26 and 36 at pseudotime 0.16 to create segment 37" [1] "Calculating divergence between 31 and 37 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 31 and 37 always FALSE -- setting breakpoint to end.
[1] "Calculating divergence between 23 and 37 (Pseudotime 0 to 0.16)" [1] "Calculating divergence between 2 and 37 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 2 and 37 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 34 and 37 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 34 and 37 always FALSE -- setting breakpoint to end.
[1] "Calculating divergence between 35 and 37 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 35 and 37 always FALSE -- setting breakpoint to end.
[1] "Joining segments 31 and 37 at pseudotime 0.16 to create segment 38" [1] "Calculating divergence between 23 and 38 (Pseudotime 0 to 0.16)" [1] "Calculating divergence between 2 and 38 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 2 and 38 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 34 and 38 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 34 and 38 always FALSE -- setting breakpoint to end.
[1] "Calculating divergence between 35 and 38 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 35 and 38 always FALSE -- setting breakpoint to end.
[1] "Joining segments 34 and 38 at pseudotime 0.16 to create segment 39" [1] "Calculating divergence between 23 and 39 (Pseudotime 0 to 0.16)" [1] "Calculating divergence between 2 and 39 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 2 and 39 always TRUE -- setting breakpoint to beginning.
[1] "Calculating divergence between 35 and 39 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 35 and 39 always FALSE -- setting breakpoint to end.
[1] "Joining segments 35 and 39 at pseudotime 0.16 to create segment 40" [1] "Calculating divergence between 23 and 40 (Pseudotime 0 to 0.16)"
R[write to console]: Difference between 23 and 40 always FALSE -- setting breakpoint to end.
[1] "Calculating divergence between 2 and 40 (Pseudotime 0 to 0.16)" [1] "Joining segments 23 and 40 at pseudotime 0.16 to create segment 41" [1] "Calculating divergence between 2 and 41 (Pseudotime 0 to 0.16)" [1] "Joining segments 2 and 41 at pseudotime 0 to create segment 42" [1] "Assigning cells to segments." [1] "Collapsing short segments." [1] "Removing singleton segments." [1] "Reassigning cells to segments." [1] "Assigning cells to nodes." [1] "Laying out tree." [1] "Adding cells to tree."
In [ ]:
%%R
#c("26","31","9","27","25","32","22", "23","21","10", "2"), pseudotime 50
plotTree(lin.tree, "stage", title="Cell Types")
In [ ]:
%%R
#c("26","31","9","27","25","32","22", "23","21","10", "2"), pseudotime 30
plotTree(lin.tree, "stage", title="Cell Types")
Run trajectory analysis for all Nematocytes¶
Generate Pseudotime Trajectories for I-cells, Neurons, Nematocytes¶
Select cell types 0, 11, 12, 17, 23 6, 9, 26 & 31 which represent the i-cells, Neural cells, and Nematocytes for downstream analysis
In [ ]:
#Subset data
#I-cells, neurons, nematocytes
big_lin = bus_fs_raw[bus_fs_raw.obs['cellRanger_louvain'].isin([31,26,6,9,0,11,12,17,23,5,10,21])]
sc.pp.filter_cells(big_lin, min_counts=0)
sc.pp.filter_genes(big_lin, min_counts=1)
sc.pp.normalize_per_cell(big_lin, counts_per_cell_after=1e4)
big_lin_copy = big_lin.copy()
sc.pp.log1p(big_lin)
big_lin.raw = sc.pp.log1p(big_lin_copy, copy=True)
sc.pp.highly_variable_genes(big_lin, n_top_genes=4000,n_bins=50)
big_lin = big_lin[:,big_lin.var['highly_variable']]
big_lin
Trying to set attribute `.obs` of view, copying.
Out[ ]:
View of AnnData object with n_obs × n_vars = 5481 × 4000
obs: 'batch', 'cellRanger_louvain', 'fed', 'orgID', 'annos', 'annosSub', 'n_counts'
var: 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
uns: 'annos_colors', 'cellRanger_louvain_colors', 'log1p', 'hvg'
Cluster and embed cells for pseudotime visualization
In [ ]:
#Large lineage PAGA embedding
big_lin_scaled = big_lin
sc.pp.scale(big_lin_scaled, max_value=10)
sc.tl.pca(big_lin_scaled, n_comps=60)
#sc.pl.pca_variance_ratio(neur_lin_scaled, log=True)
sc.pp.neighbors(big_lin_scaled,n_neighbors=50, n_pcs=15,method='gauss')
sc.tl.louvain(big_lin_scaled,resolution=1,key_added='louvain_test')#Clustering algorithm,resolution=0.5
sc.tl.paga(big_lin_scaled, groups='louvain_test',)
sc.pl.paga(big_lin_scaled, color=['louvain_test'])
In [ ]:
#Add neuron subpopulation labels
labels = []
for c in big_lin_scaled.obs_names:
if c in neuron_subpops.obs_names:
labels += [neuron_subpops[c,:].obs['louvain_neur'][0]]
else:
labels += ['rest']
big_lin_scaled.obs['louvain_neur'] = pd.Categorical(labels)
In [ ]:
sc.tl.draw_graph(big_lin_scaled, init_pos='paga')
#sc.pl.draw_graph(big_lin_scaled, color=['louvain_test','louvain_neur','cellRanger_louvain','fed'], legend_loc='on data',color_map='viridis',alpha=0.5)
In [ ]:
#Calculate pseudotime with diffusion components (dim reduction)
big_lin_scaled.uns['iroot'] = np.flatnonzero(big_lin_scaled.obs['louvain_test'] == '0')[0]
sc.tl.diffmap(big_lin_scaled,n_comps=10)
sc.tl.dpt(big_lin_scaled,n_dcs=10)
In [ ]:
sc.pl.draw_graph(big_lin_scaled, color=['louvain_test','annos','fed','dpt_pseudotime','louvain_neur'], color_map='viridis') #legend_loc='on data'
*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points. *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.
In [ ]:
sc.pl.draw_graph(big_lin_scaled, color=['annos'], color_map='viridis') #legend_loc='on data'
In [ ]:
sc.pl.draw_graph(big_lin_scaled, color=['dpt_pseudotime'], color_map='viridis') #legend_loc='on data'
In [ ]:
# 'XLOC_004102' nematocilin, XLOC_017744 as Asc-a-1
sc.pl.draw_graph(big_lin_scaled, color=['XLOC_038831','XLOC_030920','XLOC_018937','XLOC_012650','XLOC_001436','XLOC_030068',
'XLOC_004102','XLOC_005363','XLOC_039341',
'XLOC_039385','XLOC_035250'],
legend_loc='on data',color_map='plasma',s=80)
Make adata object including more variable genes to rank by their importance/relevance to the pseudotime trajectories
In [ ]:
#Make adata for doing regression (more variable genes included)
big_lin_reg = bus_fs_raw[bus_fs_raw.obs['cellRanger_louvain'].isin([31,26,6,9,0,11,12,17,23,5,10,21])]
sc.pp.filter_cells(big_lin_reg, min_counts=0)
sc.pp.filter_genes(big_lin_reg, min_counts=1)
sc.pp.normalize_per_cell(big_lin_reg, counts_per_cell_after=1e4)
big_lin_copy = big_lin_reg.copy()
sc.pp.log1p(big_lin_reg)
big_lin_reg.raw = sc.pp.log1p(big_lin_copy, copy=True)
#For shorter demonstration use 4000 option
#sc.pp.highly_variable_genes(big_lin_reg, n_top_genes=4000,n_bins=10)
sc.pp.highly_variable_genes(big_lin_reg, n_top_genes=15000,n_bins=10)
big_lin_reg = big_lin_reg[:,big_lin_reg.var['highly_variable']]
Trying to set attribute `.obs` of view, copying.
In [ ]:
#Convert pseudotime valeus to quantiles of pseudotime
big_lin_reg.obs['dpt_pseudotime'] = big_lin_scaled.obs['dpt_pseudotime']
nbins = 20 #quantiles
rangeList = range(0,nbins)
strList = [str(i) for i in rangeList]
cnido = big_lin_reg[big_lin_reg.obs['cellRanger_louvain'].isin([0,11,12,17,23,5,10,21])]
cnido.obs['quantTimeBlocks'] = pd.qcut(cnido.obs['dpt_pseudotime'],q=nbins,labels=strList)
neuro = big_lin_reg[big_lin_reg.obs['cellRanger_louvain'].isin([0,26,31,9,6])]
neuro.obs['quantTimeBlocks'] = pd.qcut(neuro.obs['dpt_pseudotime'],q=nbins,labels=strList)
Trying to set attribute `.obs` of view, copying. Trying to set attribute `.obs` of view, copying. Trying to set attribute `.obs` of view, copying.
In [ ]:
cnido.write('nemato.h5ad')
neuro.write('neuro.h5ad')
In [ ]:
big_lin_scaled
Out[ ]:
AnnData object with n_obs × n_vars = 5481 × 3999
obs: 'batch', 'cellRanger_louvain', 'fed', 'orgID', 'annos', 'annosSub', 'n_counts', 'louvain_test', 'louvain_neur', 'dpt_pseudotime'
var: 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'annos_colors', 'cellRanger_louvain_colors', 'log1p', 'hvg', 'pca', 'neighbors', 'louvain', 'paga', 'louvain_test_sizes', 'louvain_test_colors', 'draw_graph', 'iroot', 'diffmap_evals', 'fed_colors', 'louvain_neur_colors'
obsm: 'X_pca', 'X_draw_graph_fa', 'X_diffmap'
varm: 'PCs'
obsp: 'distances', 'connectivities'
In [ ]:
#Create embedding for plotting pseudotime genes
big_lin_reg.uns['paga'] = big_lin_scaled.uns['paga']
big_lin_reg.uns['draw_graph'] = big_lin_scaled.uns['draw_graph']
big_lin_reg.obsm['X_draw_graph_fa'] = big_lin_scaled.obsm['X_draw_graph_fa']
In [ ]:
sc.pl.draw_graph(big_lin_reg, color=['XLOC_019434','XLOC_018937'],
legend_loc='on data',color_map='plasma')
In [ ]:
big_lin_reg.write('pseudoEmbed_fullLineage.h5ad')
Run Regression¶
Will take a while. Can plot with saved results instead of re-running model or run with fewer genes
In [ ]:
#Run regression
result_cnido = runRegr_dptPseudotime(cnido,cnido,'quantTimeBlocks')
result_neuro = runRegr_dptPseudotime(neuro,neuro,'quantTimeBlocks')
0.9398783063776021 0.8655305326474104
In [ ]:
#Make dataframe from regression results
cnido_gene_res = resToDF(result_cnido,cnido)
neuro_gene_res = resToDF(result_neuro,neuro)
2305 2007
In [ ]:
cnido_gene_res = annotateResDF(cnido_gene_res)
neuro_gene_res = annotateResDF(neuro_gene_res)
cnido_gene_res.head()
Out[ ]:
| Genes | Mean | Std | orthoGene | orthoDescr | pantherID | pantherDescr | goTerms | |
|---|---|---|---|---|---|---|---|---|
| 11547 | XLOC_036353 | 0.061569 | 0.003140 | NA | NA | [PTHR23050:SF330] | [RE52086P] | [GO:0019722,GO:0009987,GO:0007165,GO:0006928,G... |
| 1019 | XLOC_002572 | 0.024767 | 0.003579 | NA | NA | NA | NA | [nan] |
| 618 | XLOC_001437 | 0.024244 | 0.002219 | NA | NA | NA | NA | NA |
| 616 | XLOC_001436 | 0.017474 | 0.001233 | NA | NA | [PTHR35378] | [UNNAMED PRODUCT] | [nan] |
| 12112 | XLOC_037906 | 0.016471 | 0.002140 | NA | NA | [PTHR11544:SF14] | [Y-BOX-BINDING PROTEIN 3] | [nan] |
In [ ]:
cnido_gene_res.to_csv('cnido_stem_lineage_pseudotime_genes.csv')
neuro_gene_res.to_csv('neuro_stem_lineage_pseudotime_genes.csv')
Plot Results of Regression (Pseudotime Heatmaps)¶
In [14]:
def returnVal(i):
if i == i:
i= i.replace("[","")
i = i.replace("]","")
i= i.replace("'","")
i = i.replace("'","")
return i
else:
return 'nan'
In [15]:
cnido_gene_res = pd.read_csv('D1.1808')
print(cnido_gene_res.head())
Unnamed: 0 ... goTerms 0 11547 ... ['GO:0019722,GO:0009987,GO:0007165,GO:0006928,... 1 1019 ... [nan] 2 618 ... NaN 3 616 ... [nan] 4 12112 ... [nan] [5 rows x 9 columns]
In [16]:
cnido_gene_res.columns
Out[16]:
Index(['Unnamed: 0', 'Genes', 'Mean', 'Std', 'orthoGene', 'orthoDescr',
'pantherID', 'pantherDescr', 'goTerms'],
dtype='object')
In [17]:
neuro_gene_res = pd.read_csv('D1.1807')
print(neuro_gene_res.head())
cnido = anndata.read('D1.1805')
neuro = anndata.read('D1.1806')
print(neuro)
Unnamed: 0 ... goTerms
0 8965 ... [nan]
1 4901 ... NaN
2 4254 ... [nan]
3 4124 ... [nan]
4 11788 ... ['GO:0045298,GO:0005856,GO:0005200,GO:0005622,...
[5 rows x 9 columns]
AnnData object with n_obs × n_vars = 2662 × 14999
obs: 'batch', 'cellRanger_louvain', 'fed', 'orgID', 'annos', 'annosSub', 'n_counts', 'dpt_pseudotime', 'quantTimeBlocks'
var: 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
uns: 'annos_colors', 'cellRanger_louvain_colors', 'hvg'
Plot expression of genes of interest over time, after ordering cells by pseudotime coordinates so that expression is shown over 'time'
In [18]:
#Order cells by pseudotime (0 --> 1)
orderCnido = orderTimeClus(cnido)
orderNeuro = orderTime(neuro)
In [19]:
#Plot interesting genes from ranking over time
neuroPeps = ['XLOC_042761','XLOC_019434','XLOC_017097','XLOC_030120']
n_names = ['*PP1','*PP5','*PP11','PP17']
early = ['XLOC_038831','XLOC_041836','XLOC_010226','XLOC_041854','XLOC_045448'] #'XLOC_041854','XLOC_045448'
e_names = ['Semaphorin','NACA2','aGPCR 3','LRR protein 1','LRR protein 2']
late = ['XLOC_034623','XLOC_008333','XLOC_040506','XLOC_012560','XLOC_044075',
'XLOC_043514','XLOC_034910','XLOC_001206',
'XLOC_003576','XLOC_002941','XLOC_008328','XLOC_044176','XLOC_017070'] #'XLOC_034910','XLOC_040506'
l_names = ['SIX-like','ACTR3','CAB39','Clytia-Specific 5','ASNS',
'POLR1A','NCALD','aGPCR 1',
'KCNA6','aGPCR 2','TRIO','Clytia-Specific 6','aGPCR 4']
total = early+neuroPeps+late
total_names = e_names+n_names+l_names
In [20]:
set(neuroPeps).intersection(late)
Out[20]:
set()
In [21]:
toAddNeuroDict_Other = {}
for i in range(0,len(total)):
toAddNeuroDict_Other[total[i]] = total_names[i]
In [ ]:
# labels= [toAddNeuroDict_Other[i] for i in total]
# orderNeuro = orderNeuro[:,total]
# orderNeuro.var['names'] = labels
# sc.set_figure_params(scanpy=True, fontsize=12)
# sc.pl.heatmap(orderNeuro, total_names, groupby=None,
# show_gene_labels=True,use_raw=False,standard_scale='var',
# interpolation='gaussian',cmap='plasma',swap_axes=True,gene_symbols= 'names')
Final Pseudotime Plots¶
Plots include a mixture of neuro-development related genes
In [43]:
#Add more pseudotime ranked genes for heatmap
toAddNeuro = ['XLOC_037011','XLOC_031923','XLOC_010412','XLOC_030920','XLOC_026271','XLOC_025094','XLOC_018937','XLOC_037278','XLOC_009173','XLOC_033489',
'XLOC_017744','XLOC_045293','XLOC_033337','XLOC_012650']
toAddNeuroNames = ['TUBA4A','PCNA','GLUL','bHLH TF 1','DCX','CALR','bHLH TF 2','TAGLN-2','H2AFV','PRDX4',
'Asc-a-1','*SOX-10','CL1','Clytia-Specific 4']
toAddNeuroDict = {}
for i in range(0,len(toAddNeuro)):
toAddNeuroDict[toAddNeuro[i]] = toAddNeuroNames[i]
#Add later timepoint genes
toAddCnido = ['XLOC_044122','XLOC_008048','XLOC_030068','XLOC_011074','XLOC_041365','XLOC_041836','XLOC_011949','XLOC_015554',
'XLOC_039385','XLOC_045474',
'XLOC_004102','XLOC_043404','XLOC_005363','XLOC_039341',
'XLOC_017845','XLOC_018677','XLOC_011922','XLOC_003773','XLOC_011100']
toAddCnidoNames = ['*Mcol3/4','Mcol','*Dkk3','TUBA1C','TF IIIA','NACA2','BOP1','Mos3',
'M14-peptidase','SOCS-like',
'*Nematocilin','WW-domain','Clytia-Specific 3','Sans/USH-1G',
'Znf845','*NOWA','Whirlin','Harmonin','PKD2-like']
toAddCnidoDict = {}
for i in range(0,len(toAddCnido)):
toAddCnidoDict[toAddCnido[i]] = toAddCnidoNames[i]
#add XLOC_045474 SOCS-like
In [79]:
#Determine where var_names is in list of genesToPlot
allGenes = {**toAddCnidoDict, **toAddNeuroDict}
allGenes = {**allGenes,**toAddNeuroDict_Other} #'XLOC_002941','XLOC_043514','XLOC_037011','XLOC_040506','XLOC_003576',
forPlotNeuro = ['XLOC_041836','XLOC_026271','XLOC_031923','XLOC_010226',
'XLOC_018937','XLOC_045293',
'XLOC_034623','XLOC_040506','XLOC_012560','XLOC_030920','XLOC_017744',
'XLOC_038831','XLOC_001206','XLOC_042761','XLOC_017097',
'XLOC_002941','XLOC_030120','XLOC_019434',
'XLOC_034910','XLOC_012650']
forPlotNeuro = [i for i in forPlotNeuro if i in list(neuro_gene_res.Genes)]
#Mature nematocytes genes
forPlotCnido = ['XLOC_017845','XLOC_011949','XLOC_011074','XLOC_041836','XLOC_015554','XLOC_041365','XLOC_008048','XLOC_044122','XLOC_030068','XLOC_039385','XLOC_045474',
'XLOC_011100',
'XLOC_004102','XLOC_043404','XLOC_005363','XLOC_039341',]
#forPlotCnido = [i for i in forPlotCnido if i in list(cnido_gene_res.Genes)]
In [80]:
cnidoRaw = bus_fs_raw[bus_fs_raw.obs['cellRanger_louvain'].isin([0,11,12,17,23,5,10,21])]
cnidoRaw
Out[80]:
View of AnnData object with n_obs × n_vars = 4094 × 46716
obs: 'batch', 'cellRanger_louvain', 'fed', 'orgID', 'annos', 'annosSub'
uns: 'annos_colors', 'cellRanger_louvain_colors'
In [81]:
cnido
Out[81]:
AnnData object with n_obs × n_vars = 4094 × 14999
obs: 'batch', 'cellRanger_louvain', 'fed', 'orgID', 'annos', 'annosSub', 'n_counts', 'dpt_pseudotime', 'quantTimeBlocks'
var: 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
uns: 'annos_colors', 'cellRanger_louvain_colors', 'hvg'
In [82]:
cnidoRaw.obs['dpt_pseudotime'] = cnido.obs['dpt_pseudotime']
cnidoRaw.obs['quantTimeBlocks'] = cnido.obs['quantTimeBlocks']
sc.pp.log1p(cnidoRaw)
Trying to set attribute `.obs` of view, copying.
In [83]:
labelsCnido = [allGenes[i] for i in forPlotCnido]
labelsNeuro = [allGenes[i] for i in forPlotNeuro]
#Order cells by pseudotime (0 --> 1)
orderCnido = orderTimeClus(cnidoRaw)
orderNeuro = orderTime(neuro)
orderCnido = orderCnido[:,forPlotCnido]
orderCnido.var['names'] = labelsCnido
orderNeuro = orderNeuro[:,forPlotNeuro]
orderNeuro.var['names'] = labelsNeuro
Trying to set attribute `.var` of view, copying. Trying to set attribute `.var` of view, copying.
In [84]:
sc.set_figure_params(scanpy=True, fontsize=12)
sc.pl.heatmap(orderCnido, labelsCnido, groupby=None,
show_gene_labels=True,use_raw=False,standard_scale='var',
interpolation='antialiased',cmap='plasma',swap_axes=True,gene_symbols='names',figsize=(8,4),
save='cnido_gauss_BigLinTimeGenes.pdf')
WARNING: saving figure to file figures/heatmapcnido_gauss_BigLinTimeGenes.pdf
In [ ]:
orderCnido = orderTimeClus(cnido)
sc.set_figure_params(scanpy=True, fontsize=12)
sc.pl.heatmap(orderCnido, list(cnido_gene_res.Genes[0:50]), groupby=None,
show_gene_labels=True,use_raw=False,standard_scale='obs',
interpolation='gaussian',cmap='plasma',swap_axes=True,
save='all_cnido_gauss_BigLinTimeGenes.pdf')
WARNING: saving figure to file figures/heatmapall_cnido_gauss_BigLinTimeGenes.pdf
In [ ]:
sc.set_figure_params(scanpy=True, fontsize=12)
sc.pl.heatmap(orderNeuro, labelsNeuro, groupby=None,
show_gene_labels=True,use_raw=False,standard_scale='obs',
interpolation='antialiased',resample= True,filterrad =.04,filterNorm = True, cmap='plasma',swap_axes=True,gene_symbols='names',figsize=(5,4),
save='neuro_gauss_BigLinTimeGenes.pdf') #Change normalization (z-score), don't need relative expression here
WARNING: saving figure to file figures/heatmapneuro_gauss_BigLinTimeGenes.pdf